Stages of Alzheimers Utilizing Machine Learning Techniques

Stages of Alzheimers Utilizing Machine Learning Techniques Abstract: Alzheimer’s disease (AD) is the general type of dementia that affects the elderly population globally. An accurate and early diagnosis of Alzheimer is crucial for the treatment of patients suffering from AD. In this paper, two different classifiers, SVM (Support Vector Machine) and an ANFIS (Adaptive Neuro Fuzzy Inference System) have been employed to classify patients between AD control, mild control and normal control. The system employed MRI (Magnetic Resonance Imaging) data obtained from the ADNI dataset of 150 subjects consisting of 75 normal controls, 50 mild controls and 25 AD controls. Initially, image processing techniques like segmentation and feature extraction are applied on these MRI images to enhance the classification accuracy. The segmentation is performed using k-means clustering and a GLCM (Gray Level Co-Occurrence Matrix) are used to extract the 2D features of the left ventricle of the brain. The extracted features are then utilized to train the classifiers and the results obtained from both classifiers are then compared. It is shown that the classification accuracy of ANFIS is more when compared to that of SVM classifier. Keywords: Alzheimer, MRI (Magnetic Resonance Imaging), ANFIS (Adaptive Neuro Fuzzy Inference System), SVM (Support Vector Machine). 1. Introduction Alzheimer’s disease is a neurodegenerative syndrome [1] of the brain tissues that results in progressive and permanent loss of mental function. The disease generally starts with mild indications and ends with severe damage in brain. The pathophysiology of the disease is associated with the damage and death of the neurons, originating in the hippocampus region of the brain that is involved with learning and memory, then atrophy impacts the whole brain. According to epidemiological information Alzheimer affects approximately 26 million people all over the world. In order to give proper care to AD patients, it is vital to measure the amount of atrophy present in the cerebral cortex during the initial stages of AD. The early detection of these diseases can greatly enhance diagnosis. But, diagnosis of this disease depends on the history, neuropsychological tests and clinical assessment. However, the clinical assessment is biased and the neuropsychological test does not provide high accuracy for early stage detection of the disease. In addition to neuropsychological analysis, structural imaging is greatly utilized in order to provide support to AD diagnosis. The whole brain approach utilized for describing the brain atrophy might be capable of differentiating between AD and MCI (mild cognitive impairment) patients. Recent researches [1, 2] show that the analysis of brain scan images is more consistent and subtle in identifying the presence of Alzheimer’s disease than the conventional cognitive assessment. In this circumstance, several machine learning approaches have been presented in order to perform neuroimaging analysis for classification of AD. In addition, all these approaches require training sets that is well categorized structure in order to classify each new subject that belongs to the test set. Recently MRI data have become center of several machine learning techniques for classifying subjects as CN vs. AD or CN vs. MCI. The focus of this paper is to classify between the different stages of AD utilizing machine learning techniques. Here, all the MRI brain scan images are segmented using k means clustering and the 2D shape features of the ventricles are obtained using GLCM based feature extraction. Then the extracted features are utilized for classification. First, an SVM based classifier is employed to classify the test data into three categories: normal, mild and AD. Second, an ANFIS based classifier is utilized for classification. Finally, the results of the two classifiers are compared and have been shown that ANFIS classifier outperforms SVM classifier. 2. Related Works Alzheimer’s disease (AD) classification is vital for early detection and diagnosis of the disease. Several studies explored machine learning techniques and artificial intelligence for detecting the cerebral changes and differentiate between normal aging and AD patients [1-3]. In [4] a support vector machine (SVM) based machine learning approach has been utilized for automatic classification entire brain anatomical MRI data to differentiate between elderly control and AD control patients. In this study, 16 patients with AD control and 22 patients with elderly control were used. Depending upon the gray matter characteristics extracted from region of interest (ROI), the SVM algorithm is used for classifying the subjects and the arithmetic procedures are based on bootstrap resampling in order to ensure the strength of the results. In [5] a local patch based subspace ensemble approach has been proposed that constructs several different classifiers depending on the various subsets of local patches and they are combined for robust and more accurate classification. Here, every brain image is segmented into number of local patches and the subset of patches is selected from the patch pool and a sparse representation based classifier technique has been used in order to construct a weak classifier. The multiple weak classifiers are then combined for making final decision. [6] A framework for classifying Alzheimer’s disease utilizing ADNI dataset is presented. The framework fuses overlap based and registration based similarity measures that are enhanced employing a self-smoothing operator. These enhanced metrics are then employed for the classification of Alzheimer disease. In [7] an automatic classification system for recognizing AD in MRI (structural Magnetic Resonance Imaging) has been developed. The system utilizes visual content description of anatomical brain structure (hippocampal region) and fuses two biomarkers CSF and hippocampus in order to enhance the classification accuracy. It is shown that the classification accuracy is more in case of fusion than when utilizing CSF volume or visual features separately. In [8] support vector machines (SVM) were assessed to determine whether data combined from various scanners would provide effective classification. Here, a linear SVM has been employed to classify GM (grey matter) portion of T1 weighted MR image. The results show that about 96% of clinically verified AD patients were accurately classified exploiting the entire brain image. [9] Classified between healthy, MCI and AD patients with the help of support vector machine (SVM). The author also analyzed the accuracy of classification when several a natomical brain regions and various image modalities are combined. Therefore, global and regional grey matter, regional asymmetry coefficients, Ti- quantitative MRI data and regional with matter volumes are combined. It shows that an accuracy of 88.3% in case of CTL vs. AD and 81.8 % in case of CTL vs. MCI was attained. In [10] a binary SVM has been proposed to classify patients between mild cognitive impairment and elderly control subjects from MRI images. This approach utilized a Java Agent DEvelopement Framework (JADE) in order to reduce the computation time. 3. Materials and Methods In this section, the data set and methods utilized in this study as well as the description of the proposed framework depicted in fig 1 are presented. 3.1 Subjects The data employed in this study were obtained from ADNI (Alzheimers disease Neuroimaging Initiative) database [11]. ADNI utilizes biomarker measures and neuroimaging in order to track the changes taking places in the brain of the subjects under study for diagnosing AD at an early stage. Fig 1 Block Diagram of the step involved in the classification of stages of AD 3.2 Image Preprocessing The collected T1 weighted MRI images were free from noise, missing data and outliers. In preprocessing step all the MRI brain images are segmented into VM, GM, CSF and Ventricle tissues that represent vital information about brain degeneration disease. A clustering based segmentation approach has been employed for this purpose. The k means clustering is exploited in order to extract the VM, GM and CSF features the entire MRI brain image. It partitions the data points into k clusters [12] based on the inherent distance between the data points. The intent is to minimize inter cluster variance. For a healthy MRI brain image, k is usually three (corresponding to grey matter, white matter and CSF). After segmenting the MRI brain images into GM, WM and CSF, morphological operations are applied to obtain the binary ventricle tissue. Here, morphological operators such as erosion and dilution are applied. 3.3 Feature Extraction In order to accurately classify AD patients ventricle shape features are extracted. In this work, the 2D shape features are extracted from the ventricles based on Gray-Level Co-occurrence Matrix (GLCM) feature extraction. This method computes the co-occurrence matrix of each image present in the database by calculating how frequently pixel x with certain intensity value take place in relation with other pixel y at a specific orientation ÃŽ ¸ and distance d. The eleven features calculated from every co-occurrence matrix, generates set of feature vectors. These feature vectors include contrast, homogeneity, energy, correlation, mean, variance, rectangularity, elongation, circularity, area and perimeter and listed in table 1. Table 1: Extracted Features

The Social Construction of Gender and Sexuality Essay -- Gender, argum

According to Johnny Weir, â€Å"Masculinity is what you believe it to be... [it is] all by perception, [I believe] masculinity and femininity is something that is very old-fashioned... [there is a] whole new generation of people who aren’t defined by their race or their sex or who they like to sleep with.† This statement exemplifies the definition of gender as a concept; gender is the expectations of a sex according to the culture of society. Sexuality, within this definition of gender, reflects society’s expectations, which are created in relation to the opposite sex. The variances between cultures means that gender expectations change within different cultures. These expectations put pressure on each member of society to conform and abide by the folkways of their own culture. The creation of gender expectations by society creates a restricting definition of gender roles and sexuality that vary from culture to culture. Society created the role of gender and created an emphasis on the differences between the two genders. Alma Gottlieb states: â€Å"biological inevitability of the sex organs comes to stand for a perceived inevitability of social roles, expectations, and meanings† (Gottlieb, 167). Sex is the scientific acknowledgment that men and women are biologically different; gender stems from society’s formation of roles assigned to each sex and the emphasis of the differences between the two sexes. The creation of meanings centers on the expectations of the roles each sex should fill; society creates cultural norms that perpetuate these creations. Gender blurs the lines between the differences created by nature and those created by society (Gottlieb, 168); gender is the cultural expectations of sexes, with meaning assigned to the diff... ...le or female actually identifies with their prescribed role depends on the socialization process and the way they identify with society’s expectations of them. The social construction of gender and sexuality all rely on the measure that people believe there is a difference between the two sexes, once this emphasis is taken away, is when gender roles will no longer play an integral role in the structure of society. Works Cited Gottlieb, Alma. "Interpreting Gender and Sexuality: Approaches from Cultural Anthropology." Exotic No More: Anthropology on the Front Lines. Ed. Jeremy MacClancy. Chicago: University of Chicago Press, 2002. Kilbourne, Jean. Killing Us Softly. Media Education Foundation, 2010. Lancaster, Roger N. Life is Hard, Machismo, Danger, and the Intimacy of Power in Nicaragua. Berkeley and Los Angeles: University of California Press, 1992. The Social Construction of Gender and Sexuality Essay -- Gender, argum According to Johnny Weir, â€Å"Masculinity is what you believe it to be... [it is] all by perception, [I believe] masculinity and femininity is something that is very old-fashioned... [there is a] whole new generation of people who aren’t defined by their race or their sex or who they like to sleep with.† This statement exemplifies the definition of gender as a concept; gender is the expectations of a sex according to the culture of society. Sexuality, within this definition of gender, reflects society’s expectations, which are created in relation to the opposite sex. The variances between cultures means that gender expectations change within different cultures. These expectations put pressure on each member of society to conform and abide by the folkways of their own culture. The creation of gender expectations by society creates a restricting definition of gender roles and sexuality that vary from culture to culture. Society created the role of gender and created an emphasis on the differences between the two genders. Alma Gottlieb states: â€Å"biological inevitability of the sex organs comes to stand for a perceived inevitability of social roles, expectations, and meanings† (Gottlieb, 167). Sex is the scientific acknowledgment that men and women are biologically different; gender stems from society’s formation of roles assigned to each sex and the emphasis of the differences between the two sexes. The creation of meanings centers on the expectations of the roles each sex should fill; society creates cultural norms that perpetuate these creations. Gender blurs the lines between the differences created by nature and those created by society (Gottlieb, 168); gender is the cultural expectations of sexes, with meaning assigned to the diff... ...le or female actually identifies with their prescribed role depends on the socialization process and the way they identify with society’s expectations of them. The social construction of gender and sexuality all rely on the measure that people believe there is a difference between the two sexes, once this emphasis is taken away, is when gender roles will no longer play an integral role in the structure of society. Works Cited Gottlieb, Alma. "Interpreting Gender and Sexuality: Approaches from Cultural Anthropology." Exotic No More: Anthropology on the Front Lines. Ed. Jeremy MacClancy. Chicago: University of Chicago Press, 2002. Kilbourne, Jean. Killing Us Softly. Media Education Foundation, 2010. Lancaster, Roger N. Life is Hard, Machismo, Danger, and the Intimacy of Power in Nicaragua. Berkeley and Los Angeles: University of California Press, 1992.

Case Analysis for Cirque du Soleil Case Essay

1. Describe how the touring show life cycle is supported by IT. While reading the case, you can access Cirque’s website and see actual applications (e.g. casting, ticket sales, and Cirque Club). According to this case, every step of the touring show life cycle is supported by IT. With regard to creation stage, IT plays an important role to improve this process. There is an application whose name is Open Eyes developed by IT. All of Cirque’s employees could access to Cirque’s Intranet and share interesting or surprising discoveries to others. Moreover, sharing this kind of information is significant to Cirque du Soleil because it keeps Cirque du Soleil staying top of the newest artistic rends. With regard to design stage, there are several applications created by IT to make those activities of design stage more effective and convenient. A costume application can save a lot of different measurements to costume patterns for every artist and keep this information in a database. The more important point is that this application connects with other applications to manage diverse sides of costume-making process. With regard to preparation stage, there is a Kin-Cirque application developed by IT to help artists practice reinforce their training experience. Physical fitness specialists could know how every artist’s physical condition develops. In addition, Kin-Cirque application connected with other applications to provide the exact physical measurements and needs of artists to equipment department. Then the equipment department can complete their jobs more easily. With regard to the diffusion of Cirque shows, IT has great influences on improving customer’s experience. There is an official website created by IT. People can access to this website from various parts of world. Especially the online forum where people can find special promotion, press galleries, and employment opportunities and so on was built up by IT. What is more, customers can purchase show tickets, select the seats, get the direction and so forth through an online box office. With regard to logistics stage, an e lectronic document management system and linguistic software are created by IT. They can coordinate and calibrate more than 150000 terms which depict countless equipment used at Cirque. Also they are stored in the Cirque’s databases. In addition, â€Å"IT roadcases† and VoIP technology which help Cirque du Soleil reduce a great  amount of time of touring infrastructure were developed by IT. Finally with regard to resource management, IT is widely applied to increase efficiency. IT developed an application which can assist the casting department to manage the artist bank and projects that could be old one, present one and future one. Applicants could submit their videos or performances via this application to conduct the recruitment process and their application materials would be stored in the databases. And that the Virtual Talent Scout was developed in Sep 2007. The pool of talent was enlarged by the Virtual Talent Scout. This increases Cirque’s abilities to deal with artist injury, increasing demand and accidents. In a word, to a large extent the whole touring show life cycle is supported and ameliorated by IT. 2. What was the level of alignment at Cirque du Soleil in 2008? According to the introduction of the case, the level of alignment at Cirque du Soleil is quite high in 2008.   First of all, the touring IT experts work closely with each other, even they are not in the same location. Everyone in the IT team performed as an integral and can resolve a big problem effectively and corporately. Secondly, there are a lot of servers all over the world. Employees can be easy to access to the applications and communicate with others, company, related department and so on very well. In short, in 2008 people at Cirque du Soleil can work together tightly and complete a great number of successful performances around the world. So it is a very high level of alignment at Cirque du Soleil in 2008. 3. What was the level of tension between the business needs and IT capacity? Through reading the whole case, I think the level of tension between the business needs and IT capacity is low. Because IT has improved the whole touring show life cycle and make the business effectively. Moreover, the most important point is that IT has help Cirque du Soleil to increase its customers and reduce the time of setting up the touring infrastructure which means cost of time. According achievements of IT, I could say there was a low level of tension between the business needs and IT capacity. 4. What are the key requirements, in terms of the IT architecture, of the support provided by IT at Cirque du Soleil? IT group needs to integrate the data from diverse activities. The design, ongoing improvement, growth of business, applications should meet the company’s business requirements. Moreover, IT group needs to do their best to make Cirque du Soleil operate efficiency  through information integration and management. Of course, IT group should maintain the databases and ensure all information is updated.

Tips for Moving Back in With Your Parents After College

Sure, moving back in with your parents may not have been your first choice for what to do after you graduated from college. Many people, however, move back in with their folks for a wide range of reasons. No matter why youre doing it, there are some steps you can take to make the situation easier for everyone. Set Reasonable Expectations True, you may have been able to come and go as you please, leave your room a disaster, and have a new guest over every night while you were in the residence halls, but this arrangement may not work for your folks. Set some reasonable expectations — for everyone involved — before you even step through the door. Set Some Ground Rules Alright, you may have to have a curfew so your poor mother doesnt think something terrible has happened to you if youre not home by 4:00 in the morning — but your mom also needs to understand that she cant just barge into your room without any notice. Set some ground rules as soon as possible to make sure everyone is clear on how things will work. Expect a combination of a roommate relationship and a parent/kid relationship. Yes, youve had roommates for the past several years, and you may view your parents as similar to them. Your parents, however, will always view you as their child. Do your best to keep this in mind as you figure out how things will work once you move back in. Sure, it seems ridiculous for a roommate to want to know where youre going every night. But your parents probably have a legitimate right to ask. Set a Time Frame Do you just need someplace to crash between when you graduate from college and when you start graduate school in the fall? Or do you need somewhere to live until you can save enough money on your own to get your own place? Talk about how long you plan on staying — 3 months, 6 months, 1 year — and then check back in with your parents once that time frame is up. Discuss Money, No Matter How Awkward No one really likes to talk about money. But addressing the topic with your parents — how much youll pay in rent, for food, to get back on their health insurance plan, or if the car youve been borrowing needs more gas — will help prevent a ton of problems later. Have Your Own Support Networks Ready to Go After living on your own or in the residence halls during college, living with your parents can become very isolating. Do your best to have systems in place that provide you with an outlet and support network that is separate from your parents. The Relationship Is Give and Take — Both Ways Yes, your parents are letting you stay at their place, and yes, you may pay rent to do so. But are there other ways you can help, especially if money is tight for everyone? Can you help around the house — with yard work, fix-it projects, or technical support for the computers they can never get to work right — in ways that will make your living relationship much more symbiotic? The Person Who Moves Back Is Not the Same Person Who Left Your parents may have a very specific — and outdated — idea of who is moving back in with them. Take a deep breath and do your best to remind them that, while you left the house as an 18-year-old college freshman, you are now returning as a 22-year-old, college-educated adult. Now Is the Time to Build Your Own Life — Not Pause It Just because you are at your parents place, waiting until you can move out on your own, doesnt mean your life is on pause. Volunteer, date, explore new things  and do your best to continue learning and growing instead of just waiting for your first opportunity to move on to somewhere else. Enjoy Yourself This may seem completely unthinkable if moving back in with your folks was the last thing you wanted to do. However, living at home can be a once-in-a-lifetime opportunity to finally learn your moms secret fried chicken recipe and your dads amazing way with woodworking tools. Live it up and take in as much as you can.